Piecewise exponential survival distribution

Usage

outcome_surv_pem(
  time_var,
  cens_var,
  baseline_prior,
  weight_var = "",
  cut_points
)

Arguments

time_var: character. Name of time variable column in model matrix
cens_var: character. Name of the censorship variable flag in model matrix
baseline_prior: Prior. Object of class Prior specifying prior distribution for each cut point. See Details for more information.
weight_var: character. Optional name of variable in model matrix for weighting the log likelihood.
cut_points: numeric. Vector of internal cut points for the piecewise exponential model. Note: the choice of cut points will impact the amount of borrowing between arms when dynamic borrowing methods are selected. It is recommended to choose cut points that contain an equal number of events within each interval. Please include only internal cut points in the vector. For instance, for cut points of [0, 15], (15, 20], (20, Inf], the vector should be c(15, 20). If you pass cut-points beyond the follow-up of the data, you will receive an informative warning when calling create_analysis_object() and these cut points will be ignored.

Value

Object of class OutcomeSurvPEM.

Details

Baseline Prior

The baseline_prior argument specifies the prior distribution for the baseline log hazard rate within each cutpoint. Currently, there is no option to consider different baseline priors within each cut point. The interpretation of the baseline_prior differs slightly between borrowing methods selected.

Dynamic borrowing using borrowing_hierarchical_commensurate(): the baseline_prior for Bayesian Dynamic Borrowing refers to the log hazard rate of the external control arm.
Full borrowing or No borrowing using borrowing_full() or borrowing_none(): the baseline_prior for these borrowing methods refers to the log hazard rate for the internal control arm.

Examples

es <- outcome_surv_pem(
  time_var = "time",
  cens_var = "cens",
  baseline_prior = prior_normal(0, 1000),
  cut_points = c(10, 15, 30)
)